In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals' change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the a construct (amount of X at a chosen temporal reference point) and b construct (growth in X per unit time) are not invariant with respect to choice of reference point and time unit. Latent means, variances, and covariances change with different temporal metrics. This article offers a nomenclature for describing linear latent growth models, demonstrates how latent moment parameter estimates vary as a function of changes in the a reference point and in the b growth metric, and presents a set of useful scale-free statistics for describing the results of linear latent growth modeling. Three examples are presented, two with a measured outcome and one with a latent outcome, and implications for applied and methodological researchers are presented.